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Found 22 papers, 0 papers with code
Automaton Distillation: Neuro-Symbolic Transfer Learning for Deep Reinforcement Learning
Reinforcement learning (RL) is a powerful tool for finding optimal policies in sequential decision processes.
Model-Free Robust Average-Reward Reinforcement Learning
Robust Markov decision processes (MDPs) address the challenge of model uncertainty by optimizing the worst-case performance over an uncertainty set of MDPs.
On the Robustness of AlphaFold: A COVID-19 Case Study
To measure the robustness of the predicted structures, we utilize (i) the root-mean-square deviation (RMSD) and (ii) the Global Distance Test (GDT) similarity measure between the predicted structure of the original sequence and the structure of its adversarially perturbed version.
Robust Average-Reward Markov Decision Processes
We derive the robust Bellman equation for robust average-reward MDPs, prove that the optimal policy can be derived from its solution, and further design a robust relative value iteration algorithm that provably finds its solution, or equivalently, the optimal robust policy.
Inferring Probabilistic Reward Machines from Non-Markovian Reward Processes for Reinforcement Learning
The success of reinforcement learning in typical settings is predicated on Markovian assumptions on the reward signal by which an agent learns optimal policies.
Controller Synthesis for Omega-Regular and Steady-State Specifications
Given a Markov decision process (MDP) and a linear-time ($\omega$-regular or LTL) specification, the controller synthesis problem aims to compute the optimal policy that satisfies the specification.
A Multi-criteria Approach for Fast and Outlier-aware Representative Selection from Manifolds
The problem of representative selection amounts to sampling few informative exemplars from large datasets.
Rediscovering Deep Neural Networks Through Finite-State Distributions
We propose a new way of thinking about deep neural networks, in which the linear and non-linear components of the network are naturally derived and justified in terms of principles in probability theory.
Scalable and Robust Community Detection with Randomized Sketching
Uniform random node sampling is shown to improve the computational complexity over clustering of the full graph when the cluster sizes are balanced.
Data Dropout in Arbitrary Basis for Deep Network Regularization
An important problem in training deep networks with high capacity is to ensure that the trained network works well when presented with new inputs outside the training dataset.
Coherence Pursuit: Fast, Simple, and Robust Subspace Recovery
To the best of our knowledge, this is the first provable robust PCA algorithm that is simultaneously non-iterative, can tolerate a large number of outliers and is robust to linearly dependent outliers.
Innovation Pursuit: A New Approach to the Subspace Clustering Problem
Remarkably, the proposed approach can provably yield exact clustering even when the subspaces have significant intersections.
Subspace Clustering via Optimal Direction Search
This letter presents a new spectral-clustering-based approach to the subspace clustering problem.
Spatial Random Sampling: A Structure-Preserving Data Sketching Tool
Random column sampling is not guaranteed to yield data sketches that preserve the underlying structures of the data and may not sample sufficiently from less-populated data clusters.
Low Rank Matrix Recovery with Simultaneous Presence of Outliers and Sparse Corruption
Our approach hinges on the sparse approximation of a sparsely corrupted column so that the sparse expansion of a column with respect to the other data points is used to distinguish a sparsely corrupted inlier column from an outlying data point.
Robust and Scalable Column/Row Sampling from Corrupted Big Data
Conventional sampling techniques fall short of drawing descriptive sketches of the data when the data is grossly corrupted as such corruptions break the low rank structure required for them to perform satisfactorily.
Coherence Pursuit: Fast, Simple, and Robust Principal Component Analysis
As inliers lie in a low dimensional subspace and are mostly correlated, an inlier is likely to have strong mutual coherence with a large number of data points.
Innovation Pursuit: A New Approach to Subspace Clustering
This paper presents a new approach dubbed Innovation Pursuit (iPursuit) to the problem of subspace clustering using a new geometrical idea whereby subspaces are identified based on their relative novelties.
Formalized Quantum Stochastic Processes and Hidden Quantum Models with Applications to Neuron Ion Channel Kinetics
A new class of formal latent-variable stochastic processes called hidden quantum models (HQM's) is defined in order to clarify the theoretical foundations of ion channel signal processing.
Randomized Robust Subspace Recovery for High Dimensional Data Matrices
This paper explores and analyzes two randomized designs for robust Principal Component Analysis (PCA) employing low-dimensional data sketching.
High Dimensional Low Rank plus Sparse Matrix Decomposition
In this paper, a scalable subspace-pursuit approach that transforms the decomposition problem to a subspace learning problem is proposed.
Sparse Signal Processing with Linear and Nonlinear Observations: A Unified Shannon-Theoretic Approach
These mutual information expressions unify conditions for both linear and nonlinear observations.
